Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes

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Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes

Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of resampling strategies and classifiers for imbalanced classification tasks. Existing AutoCIL methods focus solely on...

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Bibliografiske detaljer
Main Authors:	Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao
Format:	Article
Sprog:	engelsk
Udgivet:	IEEE 2025-01-01
Serier:	IEEE Access
Fag:	Class imbalance learning multi-objective optimization Bayesian optimization automated machine learning meta-learning Gaussian processes
Online adgang:	https://ieeexplore.ieee.org/document/11087233/
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